| Paper ID | IVMSP-17.3 |
| Paper Title |
AN ADAPTIVE PART-BASED MODEL FOR PERSON RE-IDENTIFICATION |
| Authors |
Xipeng Lin, Yubin Yang, Nanjing University, China |
| Session | IVMSP-17: Looking at People |
| Location | Gather.Town |
| Session Time: | Wednesday, 09 June, 16:30 - 17:15 |
| Presentation Time: | Wednesday, 09 June, 16:30 - 17:15 |
| Presentation |
Poster
|
| Topic |
Image, Video, and Multidimensional Signal Processing: [IVSMR] Image & Video Sensing, Modeling, and Representation |
| IEEE Xplore Open Preview |
Click here to view in IEEE Xplore |
| Virtual Presentation |
Click here to watch in the Virtual Conference |
| Abstract |
Existing part-based models for person Re-IDentification(Re-ID) usually suffer from part-misalignment problem caused by uniform partition of feature maps. The performances of part-based model are highly dependent on the semantically-aligned parts of the query and gallery images. However, misalignments occur very commonly in person Re-ID tasks due to the variations of viewpoints and object distances. To address the part-misalignment problem and learn a more discriminative embedding for person Re-ID, we propose a novel Adaptive Part-based Model (APM), which adaptively partition the extracted feature maps by a partition-aware module to learn an embedding. The proposed adaptive partition method is very robust to the variations of the pedestrian scale and effective in resolving the part-misalignment problem. Experimental results on three commonly used datasets, including Market-1501, DukeMTMC-reID and CUHK03, clearly demonstrate that the proposed method achieves the state-of-the-art performance. |
